ComfyUI Node: Merge Mask Batches 🎥🅥🅗🅢

Merge Mask Batches 🎥🅥🅗🅢:

The VHS_MergeMasks node is designed to combine two mask tensors into a single unified mask. This is particularly useful in video processing and AI art creation where multiple masks need to be merged for further processing or analysis. The node intelligently handles masks of different dimensions by scaling them according to a specified strategy, ensuring that the final merged mask maintains the desired properties and dimensions. This functionality is essential for creating complex mask compositions and ensuring consistency across different frames or images in a video sequence.

Merge Mask Batches 🎥🅥🅗🅢 Input Parameters:

mask_A

mask_A is the first mask tensor to be merged. It serves as one of the primary inputs and its dimensions may influence the scaling of the second mask depending on the chosen merge strategy. This parameter is crucial as it forms one half of the final merged mask.

mask_B

mask_B is the second mask tensor to be merged. Similar to mask_A, its dimensions and properties are considered during the merging process. The interaction between mask_A and mask_B determines the final output mask.

merge_strategy

merge_strategy defines how the node should handle masks of different dimensions. Options include MATCH_A, MATCH_B, MATCH_SMALLER, and MATCH_LARGER. This parameter is essential for ensuring that the masks are scaled appropriately before merging, based on the desired outcome.

scale_method

scale_method specifies the method used for scaling the masks when their dimensions do not match. This parameter ensures that the scaling process maintains the quality and integrity of the masks, which is critical for accurate merging.

crop

crop determines how the masks should be cropped during the scaling process. This parameter helps in managing the spatial alignment of the masks, ensuring that the final merged mask is correctly aligned and free from unwanted artifacts.

Merge Mask Batches 🎥🅥🅗🅢 Output Parameters:

MASK

MASK is the resulting tensor after merging mask_A and mask_B. This output represents the combined mask, which can be used for further processing or analysis in your video or image workflow.

count

count indicates the number of masks that were merged. This output provides a simple verification of the merging process, ensuring that the expected number of masks have been combined.

Merge Mask Batches 🎥🅥🅗🅢 Usage Tips:

• Ensure that mask_A and mask_B are properly pre-processed and free from noise to achieve the best merging results.
• Choose the merge_strategy based on the relative sizes of your masks to maintain the desired aspect ratio and resolution.
• Experiment with different scale_method options to find the one that best preserves the quality of your masks during scaling.

Merge Mask Batches 🎥🅥🅗🅢 Common Errors and Solutions:

"Dimension mismatch between masks"

• Explanation: This error occurs when the dimensions of mask_A and mask_B are incompatible and cannot be scaled according to the chosen strategy.
• Solution: Verify the dimensions of both masks and ensure they can be scaled appropriately. Adjust the merge_strategy or pre-process the masks to have compatible dimensions.

"Invalid merge strategy"

• Explanation: This error is raised when an unsupported merge strategy is provided.
• Solution: Ensure that the merge_strategy parameter is set to one of the supported options: MATCH_A, MATCH_B, MATCH_SMALLER, or MATCH_LARGER.

"Scaling method not recognized"

• Explanation: This error occurs when an unrecognized scaling method is specified.
• Solution: Check the scale_method parameter and ensure it is set to a valid scaling method supported by the node.